New genomic-based tools have allowed researchers to take the study of embryogenesis to the next level. Now, researchers have visualized the development of Drosophila in greater detail than ever before. More specifically, how chromatin accessibility and gene expression shift during Drosophila embryogenesis. This “ungapped” single-cell map of embryo development is the most complete and detailed in any animal to date.
The study, published in Science, is titled, “The continuum of Drosophila embryonic development at single-cell resolution.”
The study harnesses data from over one million embryonic cells spanning all stages of embryo development and represents a significant advance at multiple levels.
“Just capturing the entirety of embryogenesis—all stages and all cell types—to obtain a more complete view of the cell states and molecular changes that accompany development is a feat in its own right,” said Eileen Furlong, PhD, head of EMBL’s genome biology unit. “But what I’m really excited about is the use of deep learning to obtain a continuous view of the molecular changes driving embryonic development—down to the minute.”
Fruit fly embryonic development occurs extremely rapidly; within just 20 hours after fertilization, all tissues have formed, including the brain, gut, and heart, so the organism can crawl and eat.
“Our goal was to obtain a continuous view of all stages of embryogenesis, to capture all of the dynamics and changes as an embryo develops, not just at the level of RNA but also the control elements that regulate this process,” said co-author Stefano Secchia, a PhD student in the Furlong group.
In 2018, a team from Furlong’s lab along with a group from the lab of Jay Shendure, MD, PhD, professor, University of Washington, showed the feasibility of profiling “open” chromatin at single-cell resolution in embryos and how these DNA regions often represent active developmental enhancers. The data showed which cell types in the embryo are using which enhancers at a given time point and how this use changes over time. Such a map is essential to understand what drives specific aspects of embryonic development.
“I got really excited when I saw those results,” Furlong said. “To go beyond RNA to look upstream at these regulatory switches in single cells was something I didn’t think would be possible for a long time.”
The 2018 study set the stage to scale up dramatically using new technology developed in the Shendure lab. The team’s current work profiled open chromatin from almost one million cells and RNA from half a million cells from overlapping time-points that span the entirety of fruit fly embryo development. They performed “single-cell RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) profiling using combinatorial indexing (sci-RNA-seq and sci-ATAC-seq) to comprehensively map expressed genes and putatively active regulatory elements.”
Using machine learning—a neural network to predict the precise developmental time for every cell—the researchers took advantage of the overlapping time points to predict time at a much finer resolution.
“Even though the collected samples contained embryos with slightly different ages within a 2- or 4-hour time window, this method allows you to zoom in to any part of this embryogenesis timeline at a scale of minutes,” said Diego Calderon, PhD, a postdoctoral researcher in the Shendure lab.
Shendure added, “I was amazed how well this works. We could capture molecular changes that occur very rapidly in time, in minutes, which previous researchers had uncovered by handpicking embryos every three minutes.”
In the future, such an approach would not only be time-saving but can serve as a reference for normal embryo development to see how things might change in different mutant embryos. This could pinpoint exactly when, and in which cell type, a mutant’s phenotype arises, as the researchers showed in the muscle. In other words, this work not only helps to understand how development normally occurs but also opens the door to understanding how different mutations can mess it up.
The new predictive potential that this research portends, based on samples from much larger time windows, could be used as a framework for other model systems. For example, mammalian embryo development, in vitro cell differentiation, or even post-drug treatment in diseased cells, where gaps in sampling times can be designed to facilitate optimal time prediction at a finer resolution.
Going forward, the team plans to explore the atlas’s predictive powers.
“Combining all the new tools at our disposal in single-cell genomics, computation, and genetic engineering, I would love to see if we could predict what happens to individual cell fates in vivo following a genetic mutation,” Furlong said. “…but we’re not there yet. However, before this project, I also thought the current work wouldn’t be possible any time soon.”